Sharipbay U.Z.
DETECTING DIGITAL DECEPTION: MACHINE LEARNING'S ROLE IN EXPOSING SOCIAL MEDIA BOTS *
Аннотация:
increasing prevalence of social media bots in Kazakhstan poses a significant threat to the integrity of online discourse and democratic processes. This article explores the multifaceted challenges posed by these automated accounts, particularly in the Russian-language context, where they exploit linguistic nuances and cultural references to blend in with legitimate users. The study delves into the evolving tactics and behaviors of social media bots, highlighting their potential to amplify propaganda, spread disinformation, and manipulate public opinion. To combat this growing threat, the article examines the application of machine learning, a powerful tool that can analyze vast amounts of data to identify patterns distinguishing bots from humans. The study emphasizes the importance of feature engineering in the Russian language, highlighting linguistic features specific to the Kazakh context, and discusses the potential of the Natasha NLP library as a valuable resource for bot detection. The challenges and opportunities in developing robust bot detection models are discussed, with a focus on the need for adaptable approaches that can keep pace with the evolving nature of bot technology. The article concludes by highlighting the importance of ongoing research and collaboration to safeguard the integrity of online communication in Kazakhstan and foster a more transparent and trustworthy digital ecosystem.
Ключевые слова:
social media bots, bot detection, machine learning, Russian language, Kazakhstan, Natasha NLP, natural language processing, feature engineering, disinformation, propaganda
The digital age has ushered in unprecedented opportunities for connectivity and communication, with social media platforms playing a central role in shaping public discourse in Kazakhstan. However, this vibrant online landscape is increasingly being infiltrated by social media bots, automated accounts designed to manipulate opinions, spread disinformation, and sow discord. This article explores the growing threat of social media bots in Kazakhstan, delving into their diverse tactics, their impact on society, and the promising role of machine learning in combating this menace.The Rise of Social Bots in Kazakhstan. The proliferation of social media bots is a global phenomenon, but its impact is particularly pronounced in countries like Kazakhstan, where the online sphere plays a crucial role in political and social life. The anonymity and reach offered by platforms like VKontakte, Instagram*(* запрещено в РФ), and Facebook* (* запрещено в РФ) provide fertile ground for malicious actors seeking to exploit vulnerabilities and manipulate public opinion [1, 5]. These actors, ranging from state-sponsored entities to political operatives and commercial interests, deploy bots to achieve various objectives, including amplifying propaganda, spreading disinformation, polarizing public opinion, and manipulating political discourse [2, 10, 11]. The impact of these activities can be far-reaching, undermining democratic processes, eroding public trust, and even contributing to social unrest [4]. In the Kazakh context, where the internet penetration rate is high and social media usage is widespread [8], the influence of bots on public discourse is particularly significant.Challenges in Bot Detection. The detection of social media bots is a complex and evolving challenge. Traditional rule-based methods, which rely on manually defined criteria, are often easily circumvented by sophisticated bots that can adapt their tactics [12]. Furthermore, the unique linguistic and cultural landscape of Kazakhstan, with its dominance of the Russian language and nuanced social dynamics, requires tailored approaches for bot detection [6].Bots are becoming increasingly adept at mimicking human behavior, employing advanced techniques such as natural language generation and social engineering to evade detection. The use of "hybrid" bots, which combine automated and human-controlled elements, further complicates the detection process. Additionally, the prevalence of code-switching between Russian and Kazakh on Kazakhstani social media platforms adds another layer of complexity, making it difficult to identify linguistic patterns characteristic of bots.Machine Learning: A Game-Changer in Bot Detection. Machine learning, a branch of artificial intelligence, offers a promising solution to the challenges of bot detection. By analyzing large volumes of data and identifying patterns that distinguish bots from humans, machine learning algorithms can learn to classify accounts with high accuracy [13]. This adaptability makes machine learning a valuable tool in the ongoing arms race against increasingly sophisticated bots.Several machine learning techniques have been applied to bot detection, including supervised learning, unsupervised learning, and hybrid approaches [17, 31]. Supervised learning algorithms, such as Support Vector Machines (SVM) and Random Forests (RF), are trained on labeled datasets of bot and human accounts, enabling them to learn discriminating features and generalize this knowledge to new instances [17, 31]. Unsupervised learning techniques, such as clustering and anomaly detection, can identify bot-like behavior based on deviations from normal user patterns, without requiring labeled data. Hybrid approaches combine the strengths of both supervised and unsupervised methods to achieve more robust and accurate detection.Feature Engineering: Key to Unlocking Bot Behavior. Feature engineering, the process of selecting and transforming raw data into meaningful features for machine learning algorithms, is crucial for the success of bot detection. In the Russian-language context, feature engineering requires careful consideration of linguistic nuances, such as morphology, syntax, and vocabulary [18, 27]. Additionally, features specific to the Kazakh context, such as the use of Kazakh loanwords and references to local events, can be leveraged to enhance bot detection accuracy.Researchers have explored various feature categories for bot detection, including content-based features (e.g., linguistic patterns, sentiment analysis), metadata-based features (e.g., account age, posting frequency), and network-based features (e.g., interactions between accounts, follower networks) [20, 21, 22, 29]. By combining these diverse features, machine learning models can achieve higher accuracy in distinguishing bots from humans.Natasha NLP: A Powerful Ally in the Fight Against Bots. The Natasha NLP library, a powerful tool for processing and analyzing Russian text, plays a pivotal role in bot detection in the Kazakh context. Its capabilities, including tokenization, lemmatization, morphological analysis, and named entity recognition, enable researchers to extract meaningful features from Russian-language social media data, facilitating the development of more effective bot detection models.Several studies have already demonstrated the potential of Natasha NLP for identifying linguistic patterns characteristic of bot-generated content, such as repetitive language, grammatical errors, and lack of emotional expression [25]. By combining Natasha NLP with other tools and resources, such as sentiment analysis and network analysis tools, researchers can create more comprehensive and accurate bot detection systems.The Road Ahead: Challenges and Opportunities. The fight against social media bots in Kazakhstan is an ongoing endeavor. The evolving nature of bot technology, the linguistic complexities of the Russian language, and the unique cultural and political context of Kazakhstan pose significant challenges for bot detection efforts. However, machine learning and natural language processing tools like Natasha NLP offer promising avenues for addressing these challenges and safeguarding the integrity of online communication in Kazakhstan.Future research should focus on refining feature engineering techniques, developing more adaptable and robust machine learning models, and exploring the potential of hybrid approaches that combine different detection methods. Additionally, efforts to raise public awareness about the threat of social media bots and to educate users on how to identify and counter their influence are crucial for fostering a healthier and more transparent digital ecosystem in Kazakhstan.
Номер журнала Вестник науки №5 (74) том 3
Ссылка для цитирования:
Sharipbay U.Z. DETECTING DIGITAL DECEPTION: MACHINE LEARNING'S ROLE IN EXPOSING SOCIAL MEDIA BOTS // Вестник науки №5 (74) том 3. С. 1036 - 1042. 2024 г. ISSN 2712-8849 // Электронный ресурс: https://www.вестник-науки.рф/article/14672 (дата обращения: 17.06.2025 г.)
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